Meta, where I used to work, is developing powerful tools. I’m worried about what could happen if they’re picked up by malicious actors, says David Evan Harris, an adviser on AI ethics

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In this article, we will explore the benefits of using generative AI web tools for content creation, discuss AI platforms, and provide tips on how to get started with these innovative tools.What is Generative AI?Generative AI is a branch of artificial intelligence that focuses on creating new content or data based on existing information. It uses machine learning algorithms to analyze patterns in data and generate outputs that closely resemble the original input.

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Original article is here

 

Large language models are all the rage these days and new ones are popping up every other day. Most of these linguistic behemoths, including OpenAI’s ChatGPT and Google’s Bard, are trained on text data from all over the internet – websites, articles, books, you name it. This means that their output is a mixed bag of genius. But what if instead of the web, LLMs were trained on the dark web? Researchers have done just that with DarkBERT to some surprising results. Let’s take a look.

 

What is DarkBERT?

A team of South Korean researchers have released a paper detailing how they built an LLM on a large-scale dark web corpus collected by crawling the Tor network. The data included a host of shady sites from various categories including cryptocurrency, pornography, hacking, weaponry, and others. However, due to ethical concerns, the team did not use the data as is. To ensure that the model wasn’t trained on sensitive data so that bad actors aren’t able to extract that information, the researchers polished the pre-training corpus through filtering, before feeding it to DarkBERT.

 

If you are wondering about the rationale behind the name DarkBERT, the LLM is based on the RoBERTa architecture, which is a transformer-based model developed back in 2019 by researchers at Facebook.

 

Meta had described RoBERTa as a “robustly optimized method for pre-training natural language processing (NLP) systems” that improves upon BERT, which was released by Google back in 2018. After Google made the LLM open-source, Meta was able to improve its performance.

 

Cut to the present, the Korean researchers have improved upon the original model even further by feeding it data from the dark web over the course of 15 days, eventually arriving upon DarkBERT. The research paper highlights that a machine with an Intel Xeon Gold 6348 CPU and 4 NVIDIA A100 80GB GPUs was used for the purpose.

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Neuralangelo, a new AI model by NVIDIA Research for 3D reconstruction using neural networks, turns 2D video clips into detailed 3D structures — generating lifelike virtual replicas of buildings, sculptures and other real-world objects.

 

Like Michelangelo sculpting stunning, life-like visions from blocks of marble, Neuralangelo generates 3D structures with intricate details and textures. Creative professionals can then import these 3D objects into design applications, editing them further for use in art, video game development, robotics and industrial digital twins.

Read the full article at: blogs.nvidia.com

Figure 01 is a bipedal, AI-powered humanoid. And it wants to work in a warehouse.

 

Key to Figure’s robotics approach is its reliance on artificial intelligence to enable its robots to learn and improve their abilities. As movements and performance evolves, the robots can progress from basic lifting and carrying tasks to more advanced functions. That work will start out in the warehouse setting, doing the kind of heavy lifting that people don’t particularly enjoy.

 

“Our business plan is to get to revenue as fast as we can. And honestly that means doing things that are technically easier,” says Figure founder Brett Adcock.

 

Single-purpose robots are already common in warehouses, like the roving robots that carry boxes to shelves in Amazon fulfillment centers, and a robot created for DHL Supply Chain that unloads boxes from trucks at warehouse loading bays. Human-like robots could be a more versatile solution for the warehouse, Adcock says. Instead of relying on one robot to unload a truck and another to haul boxes around, Figure’s plan is to create a robot that can do almost anything a human worker otherwise would.

 

In some ways, it’s a narrower approach to the humanoid robot, which many other companies have sought to build. Honda’s Asimo was intended to help a range of users, from people with limited mobility to older folks needing full-time care. NASA has invested in humanoid “robonauts” that are capable of assisting with missions in space. Boston Dynamics has pushed the limits of robotics by creating humanoids that can jump and flip.

 

“Existing humanoids today have just been stunts and demos,” Adcock says. “We want to get away from that and show that they can be really useful.” Figure has produced five prototypes of its humanoid. They’re designed to have 25 degrees of motion, including the ability to bend over fully at the waist and lift a box from the ground up to a high shelf. Hands will add even more flexibility and utility. That is the plan, at least. Right now, the prototype robots are mainly just walking.

 

Adcock expects to conduct extensive testing and refinement in the coming months, getting the robots to the point where they can handle most general warehouse applications by the end of the year. A pilot of 50 robots working in a real warehouse setting is being eyed for 2024. “Hardware companies take time,” he says. “This will take 20 or 30 years for us to really build out.”

With a reflective featureless face mask that may remind some viewers of a character from G.I. Joe, Figure 01 is a sleeker vision of the humanoid robots people may now be familiar with. Atlas, the walking, jumping, and parkour-enabled robot from the research wing of Boston Dynamics, for example, has prioritized mechanical skills over aesthetics.

 

Figure’s robot bears some similarity to Tesla’s Optimus robot. Announced in 2021 during a presentation featuring a human in a spandex robot suit, Optimus also aspires to a smoother form but its latest prototype looks more science science than science fiction. Adcock says Figure’s robots use advanced electric motors, enabling smoother movement than the hydraulically run Atlas, which is allowing the prototypes to have a more natural gait while also fitting the mechanical systems into a smaller package. “We want to fool like 90% of people in a walking Turing test,” he says.

 

The concept is being built around the notion that the robot can be continually improved over time, learning new skills and eventually expanding into more complex tasks. Being able to do more, Adcock says, makes what could be a very expensive device much more affordable to build and buy. That could someday lead to Figure’s robots working in manufacturing, retail, home care, or even outer space. “I really believe that humanoids will colonize planets,” Adcock says.

 

For now, Figure is targeting the humble warehouse. “If we unveil the humanoid at some big event, it’ll just be doing warehouse work on stage the whole time,” Adcock says. “No back flips, none of that crazy parkour stuff. We just want to do real, practical work.”

Read the full article at: www.fastcompany.com

The Singularity, often referred to as the Technological Singularity, is a hypothetical point in the near future at which artificial intelligence (AI) becomes so incredibly advanced that it surpasses human intelligence, leading to rapid, unforeseeable changes in society and technology, life as we know it. The term was popularized by mathematician and computer scientist Vernor Vinge and futurist Ray Kurzweil.

 

At the Singularity, AI systems would possess the ability to autonomously learn, self-improve, and create new AI systems with very advanced capabilities and more intelligent than themselves, resulting in an exponential increase in intelligence. This self-improvement cycle could lead to AI systems that are vastly more capable and sophisticated than human minds and anything humans can even imagine.

 

The Singularity is often associated with the development of artificial general intelligence (AGI), which is an AI system that can perform any intellectual task that a human being can do. AGI is distinct from narrow AI, which is designed for specific tasks and lacks the broad cognitive abilities and adaptability of human intelligence.

 

The implications of the Singularity are the subject of much speculation and debate today. Most people who have thought about it agree that the Singularity would be a decisive turning point in human history. Some view this with awe and trepidation, almost like a religious event that will lead to everlasting life and happiness. Many believe it will bring about tremendous breakthroughs in medicine, energy, and space exploration. Others are more cautious, expressing concerns about the ethical, societal, and existential risks associated with super-intelligent AI systems. Some predict doom and gloom. Others think it is all but impossible.

 

 

Predicting the Singularity Start Date

The general caveat to this question given by all experts is that predicting when the Singularity might occur is highly uncertain, as it depends on the pace of AI research, breakthroughs in understanding human cognition, and the development of advanced hardware and software systems. Still, ChatGPT-4 has predicted a 5%-10% chance that the singularity will happen within five years, 2028.  Chat GPT-4 did so without knowledge of what has happened since September 2021, when it was last fed data.

 

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To help you reframe and enhance your perspective about growing an engaged audience, we asked top creators to share their advice.

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Don’t miss the barely mentioned news about search and AI from Google’s May 2023 announcements.– Content Marketing Institute…

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